Real time end-to-end learning system for a high frame rate video compressive sensing network

A real time end-to-end learning system for a high frame rate video compressive sensing network is described. The slow reconstruction speed of conventional compressive sensing approaches is overcome by directly modeling an inverse mapping from compressed domain to original domain in a single forward propagation. Through processing massive unlabeled video data such a mapping is learned by a neural network using data-driven methods. Systems and methods according to this disclosure incorporate a multi-rate convolutional neural network (CNN) and a synthesizing recurrent neural network (RNN) to achieve real time compression and reconstruction of video data.